Announcements

NEW The June 2018 Disease-Based Price Indexes are now available.

CORRECTION: A correction to the CPI data affects disease based price index data downloaded prior to 10/21/2016. The affected months are May 2016 through August 2016. Please download the current version for the corrected values for these months.

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Experimental Disease-Based Price Indexes

In 2013, healthcare accounted for 17.4 percent of U.S. Gross Domestic Product (GDP). Because healthcare is such a large sector, it is important that we measure its output and prices correctly. If published healthcare inflation rates are too high, then measured real output growth is too low and consumers are getting more for their healthcare dollar than the published estimates suggest. Similarly, if published healthcare inflation rates are too low, measured real output growth would be too high.

The Bureau of Labor Statistics (BLS) is committed to producing and publishing the most accurate medical price indexes possible. BLS has constructed experimental disease-based price indexes to find a better way to estimate inflation, real medical output, and real consumption.

Federal statistical agencies currently report medical data for goods and services. The National Health Expenditure Accounts (NHEA), the National Income and Product Accounts (NIPA), the Producer Price Index (PPI), and the Consumer Price Index (CPI) all report their medical statistics for physician services, hospital services, pharmaceuticals and other types of medical goods and services. However, many economists and others who analyze healthcare data believe this is not the best way to report medical statistics. In 1967, the U.S. Department of Health, Education, and Welfare noted:

"...the average consumer of medical care is not as interested in the price of a visit or hospital day as he is in the total cost of an episode of illness.[1]"

Starting with the pioneering work of Anne Scitovsky (1967), many analysts found that reporting medical statistics on a disease basis rather than a goods and services basis could provide better information on well-being. There can be large differences between the two methods because reporting on a disease basis can account for new technology that changes the use of medical resources. For example, in the 1990s a new generation of antidepressants could treat depression with fewer therapy visits. A disease-based price index for depression could account for this change in treatment, but indexes produced under the traditional approach of using medical goods and services could not.

Studies completed in the 1990's and early 2000's compute price indexes for cataracts, heart disease and depression.[2] These studies find that their disease-based price indexes grow less rapidly than indexes based on goods and services. The reason is that innovations changed how medical goods and services are used to treat these diseases. As a result, the Committee on National Statistics (CNSTAT) in 2002 published a recommendation that BLS create experimental disease-based price indexes.[3] This recommendation calls on BLS to use medical claims data to determine the quantity of physician visits, hospital visits and other inputs and use these quantities as weights in the construction of disease-based price indexes. The prices for these indexes would continue to come from the current price-collection system. While BLS would continue to generate monthly experimental disease-based price indexes from its monthly price collection system, the quantities would only be updated every year or two. The information on this page results from the CNSTAT recommendation.

When BLS set out to implement the CNSTAT recommendation, we established several criteria. First, the indexes had to be timely. Second, they needed to have a cost-of-living basis. Third, they could be used as an input for the All-Items Consumer Price Index. Fourth, there could be no additional costs or any disruption to existing statistical programs when constructing these indexes. Finally, the methods must be transparent.

Because of the criterion for no additional costs, BLS could not use medical claims for inputs because medical claims data are expensive. Instead, we use the publicly available Medical Expenditure Panel Survey (MEPS). We then get a blended data result, with prices from the BLS price index programs and quantities from MEPS.

One challenge in constructing disease-based price indexes is the choice of a method that accounts for comorbidities. Comorbidities occur when a physician office visit or a hospital visit treats a patient for more than one disease. We construct two types of disease-based price indexes that account for comorbidities differently. In one index, if a physician treats two diseases in the same visit, one visit will be allocated to each disease. In the other index, a fraction of the physician visit is assigned to each disease; the fractions must sum to one.

See the complete description of our methods to construct disease based price indexes.

Figure 1 below compares three different medical price indexes. The first is the all disease index computed under traditional goods and services method where the usage is not updated. We call this the Lowe Index. The next two are disease-based price indexes where one assigns fractions to the comorbidities as discussed in the previous paragraph and the other does not. From 1999 to April 2015, the disease-based price indexes on average grew less rapidly than the traditional Lowe index. However, there were periods when the reverse was true, particularly from 1999 to 2007. This was a period when health insurance coverage shifted from health maintenance organizations to more generous preferred provider policies. In recent years, the disease-based price indexes have grown more slowly than the Lowe index as usage has decreased when treating various diseases.

Figure 2 shows the effects of the various price indexes have on real expenditures in 2012.[4] Nominal expenditures are represented by the gray bars. Real expenditures deflated by the traditional Lowe Index are represented by the red bars, and real expenditures deflated by a disease-based price index are represented by the light blue bars. Using a disease-based price index results in higher real medical care expenditures for 2012 than using the traditional Lowe price indexes. This also increases real GDP.

Similar to BLS's currently published Lowe medical indexes, the experimental disease-based price indexes need a representative sample of medical transaction prices. The sampling of medical prices is a challenging task. Respondent participation in our price-collection programs is voluntary, and the reimbursement rates negotiated between insurers and medical providers often are proprietary. These rates are not posted for all customers to observe in the same way as, say, coffee prices in a grocery store. This puts more burden on respondents for the medical providers and on the BLS field economists who collect these prices. BLS has reduced respondent burden, and we are trying to reduce it even more. We appreciate the cooperation of the medical providers who participate in our price-collection program.

It is a great accomplishment to release these indexes in timely manner without increasing costs or disrupting our current statistical programs. BLS has found a way to use our existing products better.

BLS is not the only statistical agency that is producing statistics on a disease basis. The U.S. Bureau of Economic Analysis (BEA) has also introduced a Health Care Satellite Account, in which spending is reported by disease rather than by medical goods and services. BEA has also generated disease-based price indexes with a variety of databases.

Yet, there is still much to do. Patients consume medical goods and services to heal or be protected from disease. However, there currently is no reliable data source on the healing and prevention outcomes from medical spending. Many data users have suggested that BLS adjust our healthcare price indexes to reflect changes in the quality of the treatment outcomes that result from new technology. There are many challenges to quality adjustment, and we outline them in our methods.

Disease-based price indexes are in their infancy. We regard them as experimental because we still need to learn more from the research that we and others will conduct. As we learn and improve these indexes, BLS hopes that they will greatly enhance our understanding of the healthcare sector.

We list below additional research about healthcare price indexes. Not all the authors of the research papers and conference presentations are affiliated with BLS. We provide this information for your convenience, and this research does not necessarily reflect the views or policies of BLS.

Footnotes

[1] US Department of Health, Education and Welfare (1967), A Report to the President on Medical Care Prices, U.S. Government Printing Office, page 13.

[2] For heart disease, see Cutler et. al. (1998). For depression, see Berndt et. al. 2002. For cataracts, see Shapiro and Wilcox (1996).

[3] This is recommendation 6.1 in Mackie and Schultze (2002).

[4] We use the MEPS to get the medical spending totals and the most current year is 2012.

Data

The file below decomposed the growth in nominal expenditure by disease into the parts that come from inflation growth, population growth and prevalence growth.

The file below contains charts and the monthly history of the various disease based price indexes from January 1999 to the latest month in 2018. It contains not only the utilization adjusted disease based price indexes but also the indexes that are computed under traditional methods (the Lowe Indexes). There are the unsmoothed indexes where all the yearly quantity updates are done in January of each year and causing a jump in January and also the smoothed indexes where 1/12 of the yearly quantity adjustment is applied to each month.

Highfill, Tina, and Elizabeth Bernstein, (2014), "Using Disability Adjusted Life Years to Value the Treatment of Thirty Chronic Conditions in the United States From 1987–2010." In General Conference of the International Association for Research in Income and Wealth. Rotterdam, The Netherlands: South Holland.

Konüs, A.A., (1939), "The Problem of the True Index of the Cost of Living," Econometrica, 7, 10—29.

Murphy, Kevin M., and Robert H. Topel. 2006. "The Value of Health and Longevity." Journal of Political Economy 114, no. 5 (October): 871–903. National Research Council. 2010. Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of Their Improvement. Washington, DC: The National Academies Press.

Pinkovskiy, Maxim, (2014), "The Impact of the Political Response to the Managed Care Backlash on Health Care Spending: Evidence From State Regulations of Managed Care" Working Paper. New York, NY: Federal Reserve Bank of New York.

Stewart, Susan, David M Cutler, and Allison B. Rosen. 2013. "U.S. Trends in Quality—Adjusted Life Expectancy From 1987 to 2008: Combining National Surveys to More Broadly Track the Health of the Nation." American Journal of Public Health 103 (November) 78–87.

Studies in Income and Wealth, vol. 62. Chicago: University of Chicago Press.

Triplett J.E., (2001) "What's Different about Health? Human Repair and Car Repair in National Accounts and in National Health Accounts," in Medical Care Output and Productivity, eds. Cutler D.M. and Berndt E.R., University of Chicago Press, 15—96.